Phenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships. Using genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes. The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

中文翻译：

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优化的分组以提高基于基因组选择育种程序中的组记录的育种值预测的准确性

分组均值或分组总和的表型记录可以替代单个记录，因为某些记录难以测量，但具有重要的经济意义，例如饲料效率或产蛋量。基于群体记录的预测育种值的准确性随着群体成员之间关系的增加而增加。与亲缘关系更密切的动物组成群体的经典方法是基于谱系信息。当基因型信息在表型化之前可用时，其用于形成组的信息可能会进一步提高根据组记录进行预测的准确性。这项研究分析了基于基因组信息的两种分组方法：（1）在STRUCTURE软件中实现的无监督聚类，以及（2）对基因组关系进行建模的有监督聚类。使用基因组最佳线性无偏预测（GBLUP）模型，基于分组记录的遗传变异估计与基于单个记录的遗传变异估计一致。当可获得基因组信息以构成各组时，组成员之间的基因组关系系数要比采用父本半同胞和全同胞随机分组的情况高。与随机分组相比，基于基因组信息的分组方法可提高基因组估计育种值（GEBV）预测的准确性。全同胞同父异母同胞的增幅为〜1.5％，同父异母同胞异同的增幅为〜11.5％。此外，基于基因组信息的分组方法导致在由GEBV排名的顶级动物之间的coancestry系数较低。在所提出的两种方法中，监督聚类在准确性，计算要求和适用性方面均表现出色。通过添加多余的基因型后代（基因型多于后代组），与全同胞的随机分组相比，有监督聚类的优势最多可增加4.5％，与父系半同胞的随机分组相比，可监督集群的优势最多可增加14.7％ 。随着家庭规模的增加或基因组大小的减少，这一优势也随之增加。当在基因组选择育种程序中使用表型组记录时，使用基因分型信息对动物进行分组可提高选择的准确性。